Novel computational tools in machine learning open new perspectives in quantum information systems. Here we adopt the open-source programming library Tensorflow to design multi-level quantum gates including a computing reservoir represented by a random unitary matrix. In optics, the reservoir is a disordered medium or a multimodal fiber. We show that by using trainable operators at the input and at the readout, it is possible to realize multi-level gates. We study single and qudit gates, including the scaling properties of the algorithms with the size of the reservoir.
翻译:机器学习量子信息系统新视角时的新计算工具。 我们在此采用开放源码编程库Tensorflow 设计多级量子门, 包括由随机单一矩阵代表的计算储量库。 在光学中, 储量是一个有障碍的介质或多式纤维。 我们显示, 通过在输入和读出时使用可培训的操作员, 有可能实现多级门。 我们研究单级和Qutit门, 包括以储量大小计算算法的缩放属性 。